Validating multi-photon quantum interference with finite data

被引:9
|
作者
Flamini, Fulvio [1 ,2 ]
Walschaers, Mattia [3 ]
Spagnolo, Nicolo [1 ]
Wiebe, Nathan [4 ,5 ,6 ]
Buchleitner, Andreas [7 ]
Sciarrino, Fabio [1 ]
机构
[1] Sapienza Univ Roma, Dipartimento Fis, Piazzale Aldo Moro 5, I-00185 Rome, Italy
[2] Univ Innsbruck, Inst Theoret Phys, Technikerstr 21a, A-6020 Innsbruck, Austria
[3] ENS PSL Res Univ, Sorbonne Univ, Coll France, Lab Kastler Brossel,CNRS, 4 Pl Jussieu, F-75005 Paris, France
[4] Microsoft Res, Stn Q Quantum Architectures & Computat Grp, Redmond, WA USA
[5] Pacific Northwest Natl Lab, Richland, WA 99352 USA
[6] Univ Washington, Dept Phys, Seattle, WA 98195 USA
[7] Albert Ludwigs Univ Freiburg, Phys Inst, Hermann Herder Str 3, D-79104 Freiburg, Germany
基金
欧盟地平线“2020”;
关键词
Boson Sampling; multi-photon interference; validation; machine learning; quantum technologies; COHERENT ISING MACHINE; COMPUTATION; COMPLEXITY; SUPREMACY;
D O I
10.1088/2058-9565/aba03a
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Multi-particle interference is a key resource for quantum information processing, as exemplified by Boson Sampling. Hence, given its fragile nature, an essential desideratum is a solid and reliable framework for its validation. However, while several protocols have been introduced to this end, the approach is still fragmented and fails to build a big picture for future developments. In this work, we propose an operational approach to validation that encompasses and strengthens the state of the art for these protocols. To this end, we consider the Bayesian hypothesis testing and the statistical benchmark as most favorable protocols for small- and large-scale applications, respectively. We numerically investigate their operation with finite sample size, extending previous tests to larger dimensions, and against two adversarial algorithms for classical simulation: the mean-field sampler and the metropolized independent sampler. To evidence the actual need for refined validation techniques, we show how the assessment of numerically simulated data depends on the available sample size, as well as on the internal hyper-parameters and other practically relevant constraints. Our analyses provide general insights into the challenge of validation, and can inspire the design of algorithms with a measurable quantum advantage.
引用
收藏
页数:12
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